PDF(Pubmed)
Abstract：
UNASSIGNED: The speech reception threshold (SRT), synonymous with the speech recognition threshold, denotes the minimum hearing level required for an individual to discern 50% of presented speech material. This threshold is measured independently in each ear with a repetitive up-down adjustment of stimulus level starting from the initial SRT value derived from pure tone thresholds (PTTs), measured via pure-tone audiometry (PTA). However, repetitive adjustments in the test contributes to increased fatigue for both patients and audiologists, compromising the reliability of the hearing tests.
UNASSIGNED: Determining the first (initial) sound level closer to the finally determined SRT value, is important to reduce the number of repetitions. The existing method to determine the initial sound level is to average the PTTs called pure tone average (PTAv).
UNASSIGNED: We propose a novel method using a machine learning approach to estimate a more optimal initial sound level for the SRT test. Specifically, a convolutional neural network with 1-dimensional filters (1D CNN) was implemented to predict a superior initial level than the conventional methods.
UNASSIGNED: Our approach produced a reduction of 37.92% in the difference between the initial stimulus level and the final SRT value.
UNASSIGNED: This outcome substantiates that our approach can reduce the repetitions for finding the final SRT, and, as the result, the hearing test time can be reduced.
UNASSIGNED: Determining the first (initial) sound level closer to the finally determined SRT value, is important to reduce the number of repetitions. The existing method to determine the initial sound level is to average the PTTs called pure tone average (PTAv).
UNASSIGNED: We propose a novel method using a machine learning approach to estimate a more optimal initial sound level for the SRT test. Specifically, a convolutional neural network with 1-dimensional filters (1D CNN) was implemented to predict a superior initial level than the conventional methods.
UNASSIGNED: Our approach produced a reduction of 37.92% in the difference between the initial stimulus level and the final SRT value.
UNASSIGNED: This outcome substantiates that our approach can reduce the repetitions for finding the final SRT, and, as the result, the hearing test time can be reduced.
摘要：
■语音接收阈值(SRT)，与语音识别阈值同义，表示个人辨别所提供的语音材料的50％所需的最低听力水平。该阈值在每只耳朵中独立测量，从纯音阈值（PTT）得出的初始SRT值开始重复上下调节刺激水平，通过纯音测听法（PTA）测量。然而,测试中的重复调整有助于增加患者和听力学家的疲劳，损害听力测试的可靠性。
■确定第一（初始）声级更接近最终确定的SRT值，减少重复次数很重要。确定初始声级的现有方法是对称为纯音平均(PTAv)的PTT求平均。
■我们提出了一种新颖的方法，该方法使用机器学习方法来估算SRT测试的更最佳初始声级。具体来说,实现了具有一维滤波器（1DCNN）的卷积神经网络，以预测比传统方法更好的初始水平。
■我们的方法使初始刺激水平与最终SRT值之间的差异降低了37.92％。
■这一结果证实了我们的方法可以减少寻找最终SRT的重复次数，and,作为结果，听力测试时间可以减少。
■确定第一（初始）声级更接近最终确定的SRT值，减少重复次数很重要。确定初始声级的现有方法是对称为纯音平均(PTAv)的PTT求平均。
■我们提出了一种新颖的方法，该方法使用机器学习方法来估算SRT测试的更最佳初始声级。具体来说,实现了具有一维滤波器（1DCNN）的卷积神经网络，以预测比传统方法更好的初始水平。
■我们的方法使初始刺激水平与最终SRT值之间的差异降低了37.92％。
■这一结果证实了我们的方法可以减少寻找最终SRT的重复次数，and,作为结果，听力测试时间可以减少。
